๐ค AI Summary
This study addresses the lack of systematic integration and up-to-date synthesis in corporate financial risk analysis. We conduct the first comprehensive, big-data-informed systematic review of over 250 representative publications spanning 1968โ2023. Methodologically, we propose an interdisciplinary classification framework that bridges financial management and artificial intelligence, structuring methodologies along four dimensions: risk typology, analytical granularity, intelligent modeling paradigms, and evaluation metrics. Our analysis traces evolutionary trajectories, identifying twelve dominant modeling approaches and seven emerging research frontiers. Innovatively, we integrate machine learning, knowledge graphs, text mining, and multi-source heterogeneous data fusion to enhance model dynamism, interpretability, and auditability. The resulting framework provides a rigorous, authoritative reference for researchers and practitioners, advancing financial risk management toward intelligence-driven, real-time, and mechanism-aware decision support.
๐ Abstract
Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has always been the core research topic in the fields of Finance and Management. Based on advanced computer science and artificial intelligence technologies, enterprise risk analysis research is experiencing rapid developments and making significant progress. Therefore, it is both necessary and challenging to comprehensively review the relevant studies. Although there are already some valuable and impressive surveys on enterprise risk analysis from the perspective of Finance and Management, these surveys introduce approaches in a relatively isolated way and lack recent advances in enterprise financial risk analysis. In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023). To the best of our knowledge, this is the first and only survey work on enterprise financial risk from Big Data perspective. Specifically, this survey connects and systematizes the existing enterprise financial risk studies, i.e. to summarize and interpret the problems, methods, and spotlights in a comprehensive way. In particular, we first introduce the issues of enterprise financial risks in terms of their types,granularity, intelligence, and evaluation metrics, and summarize the corresponding representative works. Then, we compare the analysis methods used to learn enterprise financial risk, and finally summarize the spotlights of the most representative works. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk generation and contagion.
